2010.10.01 重组自交系，高通量测序，QTL定位 [TAG]

ORIGINAL PAPER Mapping 49 quantitative trait loci at high resolution through sequencing-based genotyping of rice recombinant inbred lines Lu Wang • Ahong Wang • Xuehui Huang • Qiang Zhao • Guojun Dong • Qian Qian • Tao Sang • Bin Han Received: 22 November 2009 / Accepted: 8 September 2010 � The Author(s) 2010. This article is published with open access at Springerlink.com Abstract Mapping chromosome regions responsible for quantitative phenotypic variation in recombinant popula- tions provides an effective means to characterize the genetic basis of complex traits. We conducted a quantita- tive trait loci (QTL) analysis of 150 rice recombinant inbred lines (RILs) derived from a cross between two cultivars, Oryza sativa ssp. indica cv. 93-11 and Oryza sativa ssp. japonica cv. Nipponbare. The RILs were genotyped through next-generation sequencing, which accurately determined the recombination breakpoints and provided a new type of genetic markers, recombination bins, for QTL analysis. We detected 49 QTL with pheno- typic effect ranging from 3.2 to 46.0% for 14 agronomics traits. Five QTL of relatively large effect (14.6–46.0%) were located on small genomic regions, where strong candidate genes were found. The analysis using sequenc- ing-based genotyping thus offers a powerful solution to map QTL with high resolution. Moreover, the RILs developed in this study serve as an excellent system for mapping and studying genetic basis of agricultural and biological traits of rice. Introduction Genetic variation of a complex trait is usually controlled by multiple loci. When studied in a recombinant population, the trait typically varies in a continuous manner. The use of molecular genetic markers decades ago enabled the detection of chromosomal regions harboring quantitative trait loci (QTL). Since then, the number of studies to map QTL has increased rapidly, fueled primarily by interests to identify the genetic control of agriculturally, medically, and ecologically important traits (Tanksley 1993; Lander and Schork 1994; Mackay 2001; Mauricio 2001). Advan- ces in molecular biology and genomic techniques have then made it possible to narrow down a QTL to a few or even a single candidate gene (Doebley et al. 1997; Frary et al. 2000; Yano et al. 2000; Grisart et al. 2002). The cloning of QTL and identification of causative mutations have opened an avenue to unlock the genetic basis of complex pheno- typic variation. Communicated by T. Sasaki. L. Wang and A. Wang contributed equally to this work. Electronic supplementary material The online version of this article (doi:10.1007/s00122-010-1449-8) contains supplementary material, which is available to authorized users. L. Wang � A. Wang � X. Huang � Q. Zhao � T. Sang � B. Han (&) National Center for Gene Research and Institute of Plant Physiology and Ecology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai 200233, China e-mail: bhan@ncgr.ac.cn G. Dong � Q. Qian State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310006, China T. Sang Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA B. Han Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China 123 Theor Appl Genet DOI 10.1007/s00122-010-1449-8 However, cloning QTL remains technically challenging. It either requires the development of near-isogenic lines (NILs) through repeatedly backcrossing with one of the mapping parents (Ashikari et al. 2005; Konishi et al. 2006; Song et al. 2007; Jin et al. 2008; Shomura et al. 2008; Xue et al. 2008; Li et al. 2006) or additional samples of natural variants for association of phenotype and candidate genes (Grisart et al. 2002; Van Laere et al. 2003; Sutter et al. 2007; Harjes et al. 2008). Positional cloning using NILs is time-consuming and labor-intensive because it takes a few generations of backcrossing to make NILs and thousands of recombinants to fine map the candidate genes. It could be prohibitively tedious and prolonged for organisms with relatively long life cycles or relatively few offspring from crosses. With regard to the other cloning strategy involving association analysis, the difficulty arises as it often relies on the presence of candidate genes with known function near the QTL. The whole-genome sequencing approach takes advan- tage of a recently developed genotyping method that uses single nucleotide polymorphisms (SNPs) detected from whole-genome sequencing of a mapping population (Huang et al. 2009). This approach could substantially reduce the amount of time and effort required for QTL mapping. The SNPs were evaluated in sliding windows to generate recombination maps for the individuals. The maps were then aligned and used to define recombination bins for the entire population. Recombination bins can serve as a new and effective type of genetic markers for QTL analysis. It is different from conventional molecular markers, such as random amplified polymorphic DNA (RAPD), restriction fragment length polymorphisms (RFLPs), insertion–deletion markers (In/Del), and simple sequence repeat (SSR), which often have uneven distri- bution and low density on whole genome and need more time on genotyping. The bins, which presumably capture all recombination events in the population, provide availably abundant markers based on dense SNPs for detailed genome-wide trait analysis. In this study, we reported high-resolution QTL mapping through sequencing-based genotyping of 150 rice recom- binant inbred lines (RILs). The population was developed from a cross between two rice cultivars with genome sequences, Oryza sativa ssp. japonica cv. Nipponbare and Oryza sativa spp. indica cv. 93-11 (Goff et al. 2002; Yu et al. 2002; International Rice Genome Sequencing Project 2005). We identified 49 QTL within relatively small genomic regions for 14 agronomic traits. With a relatively high mapping resolution, we were able to identify the candidate genes for some QTL of large or moderate effect. The new genotyping method thus greatly improved the resolution and precision of QTL mapping for complex traits. Materials and methods Mapping population The rice mapping population of 150 RILs was derived by single-seed descents from a cross between Oryza sativa ssp. indica cv. 93-11 and Oryza sativa ssp. japonica cv. Nipponbare. The population was developed in the experi- mental fields at China National Rice Research Institute in Hangzhou, Zhejiang Province, and Sanya, Hainan Prov- ince. After ten generations of self-fertilization following the initial cross, DNAs of the F11 RILs were isolated for genotyping. Phenotyping was conducted in the Hangzhou field (N 30.32�, E 120.12�) from May to October, 2008, and in the laboratory following harvest. Phenotyping Of 18 individuals of each RIL and parent grown in the field, 5 plants were randomly chosen for phenotyping. A total of 14 traits were evaluated. Traits measured directly in the field include heading date, culm diameter, plant height, flag leaf length and flag leaf width, tiller angle, tiller number, panicle length, and awn length. Traits measured in the laboratory following harvest include grain length, grain width, grain thickness, grain weight, and spikelet number per panicle (Table S1). Heading date was recorded as days from sowing to time when inflorescences had emerged above the flag leaf sheath for more than half of the individuals of line. Culm diam- eter, plant height, flag leaf length and width, and tiller angle were evaluated when panicles fully emerged. Culm diameter was measured at the thickest location of the third tiller node from the root; tiller angle was scored on a 1–6 scale (1, \10� between tiller and vertical; 6, [45�). Plant height was measured from the soil surface to the apex of the tallest panicle. On the main tiller, flag leaf length was measured from leaf blade and sheath boundary to the leaf apex; flag leaf width was measured at the widest location of the leaf. Tiller number, panicle length, and awn length were evaluated when grains fully matured. All flowered tillers of an individual were counted, the longest panicle was measured in length, and five grains located on the top of this panicle were chosen for measuring awn length. The total number of spikelets produced on the main tiller was counted. The grain related traits were measured in the laboratory after grains were detached from panicles and awns were removed from the grains. For the sampled panicles of an individual, grains were mixed and 10 grains were randomly sampled for phenotyping. Grain length, width, and thick- ness were recorded at the maximal values for each grain using an electronic digital caliper. Grain weight was Theor Appl Genet 123 initially obtained by weighing a total of 200 grains, which was then converted to 1,000-grain weight, a scale com- monly used for yield evaluation. Genotyping, linkage map, and QTL analysis A high-throughput genotyping method was previously developed and tested using these 150 rice RILs (Huang et al. 2009). The RILs were genotyped based on SNPs generated from the whole-genome resequencing. A recombination map was constructed for each RIL. The recombination maps were aligned to determine recombi- nation bins across the entire population with the minimal bin length of 100 kb adopted. Resulting bins were then treated as a genetic marker for linkage map construction using MAPMAKER/EXP version 3.0b (Lander et al. 1987). Using this linkage map and phenotypic values, QTL analysis was conducted with the composite interval map- ping (CIM) implemented in software Windows QTL Car- tographer V2.5 (Wang et al. 2007) (http://statgen.ncsu.edu/ qtlcart/WQTLCart.htm). The CIM analysis was run using Model 6 with forward and backward stepwise regression, a window size of 10 cM, and a step size of 2 cM. Experi- ment-wide significance (P \ 0.05) thresholds for QTL detection were determined with 1,000 permutations. The location of a QTL was described according to its LOD peak location and the surrounding region with 95% confidence interval calculated using WinQTLCart. The epistasis between QTL was estimated using R/qtl in the R package (http://www.rqtl.org) (Broman et al. 2003). Simulation schemes To evaluate the effect of marker density, two sets of markers with different density were simulated for QTL analysis. For the set with low marker density, 238 locations evenly distributed in the rice genome were designated with the density of 1 marker per 1.6 Mb based on physical position. Then each location was treated as one simulated marker, and the genotype of the marker was deduced from genotype of the recombination bin where the marker was located. In this way, genotypes of 150 individuals with a total of 238 simulated markers were obtained (Table S2). The set with high-marker density was simulated in the same way. The density was 1 marker per 164 kb, which generated a total of 2,330 markers. To evaluate the effect of population size, 50 and 100 lines were randomly sampled from 150 RILs five times for QTL analysis, respectively. Moreover, genotypes and phenotypes were simulated five times for each population size (from 50 to 500 individuals) for QTL analysis using the simulation module in the software WinQTLCart. In the simulation, chromosome number and marker position were imported according to 2,334 bins, and the QTL information was imported based on the 49 QTL mapped using the 150 RILs. The way to construct genetic map and QTL analysis using the simulated markers and populations was the same as that for the 150 RILs. Results Phenotypic variation Phenotypic variation of the rice RILs and parents is illus- trated in Fig. 1 and supplemental Fig. S1. Of the 14 traits evaluated, 10 showed significant differences between the indica and japonica mapping parental lines and 2 (culm diameter and tiller number) were not significantly different between the parental lines, while the significance level was not determined for heading date or tiller angle (Table S1). All traits showed transgressive segregation in the RIL population (Fig. 1). The correlation of trait variation is illustrated in Fig. 2. Significantly positive correlation is found among nine traits (green shading), awn length, grain length, culm diameter, spikelet number, plant height, panicle length, flag leaf length, flag leaf width, and heading date. This group of nine traits shows significantly negative correlation with other four traits (yellow shading), tiller angle, tiller number, grain width, and grain thickness. Specifically, grain thickness is negatively correlated with spikelet number, panicle length, flag leaf length, and heading date; grain width is negatively correlated with awn length, grain length, culm diameter, spikelet number, and panicle length; tiller number is negatively correlated with culm diameter, spikelet number, panicle length, plant height, flag leaf length, and flag leaf width; tiller angle is negatively correlated with flag leaf length and width. Between these four traits, grain thickness and grain width are significantly positive correlation. The remaining trait (purple shading), 1,000-grain weight, was positively correlated with grain thickness and grain width, and shows both positive and negative correlations with the first group of nine traits. It is positively correlated with awn length, grain length, culm diameter, panicle length, and plant height, and negatively correlated with spikelet number and heading date. Linkage map of recombination bins A linkage map was constructed using 2,334 recombination bins which was obtained from the whole-genome rese- quencing of the 150 RILs (Huang et al. 2009), which resulted in a total genetic distance of 1,539.5 cM with an average interval of 0.66 cM between adjacent bins. Theor Appl Genet 123 Fig. 1 Variation of phenotypic traits in RILs. Mean and standard deviation of the parents are indicated at the top of each histogram, with i and j representing O. sativa ssp. indica cv. 93-11 and O. sativa ssp. japonica cv. Nipponbare, respectively Theor Appl Genet 123 For each chromosome, the average genetic distance between adjacent bins ranging 0.66–0.82 cM, with the maximal distance between 2.1 and 8.3 cM (Table S3). The linkage map constructed from the bins is compared with a map generated from an F2 population of 186 indi- viduals derived from a cross between japonica cv. Nip- ponbare and indica cv. Kasalath (Harushima et al. 1998) (Table S3). This represents the rice linkage map covered with the largest number of conventional molecular markers reported to date, where we found a total of 3,235 genetic markers including RFLP, RAPD, and STS from the most updated version (http://www.gramene.org/db/cmap/map_ set_info?map_set_acc=jrgp-rflp-2000). The total genetic distance of the 12 chromosomes of these two maps is very close. The average genetic distance between adjacent bins with greater than zero distance is 0.72 cM on our map, smaller than the average of 1.03 cM for the conventional markers. The maximal genetic distance between adjacent markers is 8.3 and 15.6 cM on the bin and conventional maps, respectively. Furthermore, on the map with con- ventional markers, more than half (53.7%) of the adjacent markers have genetic distance of 0, whereas only about 7.9% of adjacent bins had zero genetic distance as calcu- lated by MAPMAKER. In addition, 76.6% of adjacent bins had genetic distance between 0.1–1 cM, whereas 31.6% of adjacent markers have this level of resolution on the con- ventional map (Fig. 3; Table S4). Therefore, the map with recombination bins has well-distributed linkage distance and higher resolution than the conventional map. QTL analysis The LOD thresholds for QTL calling were estimated from the permutation test and ranged from 2.85 for tiller number to 3.48 for flag leaf width. Based on these thresholds, a total of 40 QTL were called for the 14 traits, with pheno- typic effect (R2) of the QTL ranging 4.3–46.0% (Table 1). Considering the power of QTL detection with 150 RILs, we also reported QTL with LOD value higher than 3.0. This gives nine additional QTL, with phenotypic effect ranging 3.2–7.0% (Table 1). Thus a total of 49 QTL are detected on 12 rice chromosomes, with 1–5 QTL detected for each trait (Fig. 4). The region of each QTL identified in this study was based on the 95% confidence interval (CI) calculated using WinQTLCart (Wang et al. 2007). Of them, QTL that explained more than 10% of phenotypic effects were defined as major-effect QTL here. We totally iden- tified 10 major-effect QTL, including qTA-9, qPH-1, qFLW-4, qGL-3, qGW-5, qAL-1, qAL-3, qPH-2, qHD-3, and qCD-2. We searched the literatures and database for previously identified QTL from mapping populations also derived from crosses between indica and japonica cultivars. QTL detected in this study were compared with those previously identified by physically locations that could be clearly determined. In this condition, 18 of our QTL for 11 traits fell into the chromosomal regions containing the QTL identified in the previous studies (Table 2), including the top 5 large-effects QTL and another major-effect QTL (qHD-3). The remaining 31 were not found. These new QTL include two of the three QTL for culm diameter, two of the four QTL for plant height, two of the three QTL for flag leaf length, three of the four QTL for flag leaf width, two of the three QTL for tiller angle, one QTL for tiller number, four of the five QTL for panicle length, three of the four QTL for grain length, two of the five QTL for Fig. 2 The correlation of trait variation. Blue and red lines indicated positive and negative correlations, respectively. Solid lines P \ 0.01; dotted lines 0.01 \ P \ 0.05. AL awn length, GL grain length, CD culm diameter, SN spikelet number, PL panicle length, PH plant height, FLL flag leaf length, FLW flag leaf width, HD heading date, TA tiller angle, TN tiller number, GW grain width, GT grain thickness, TGW 1,000-grain weight Fig. 3 Comparison of chromosomal coverage between bins, high- density simulated markers, and conventional molecular markers. Bars indicate the frequency of genetic distance between adjacent markers on the linkage maps. White bars bin markers from this study; gray bars, simulated markers from this study; black bars conventional molecular markers from a previously studied rice F2 population Theor Appl Genet 123 T a b le 1 Q T L id en ti fi ed fr o m th e an al y si s o f th e ri ce re co m b in an t in b re d li n es T ra it L O D th re sh o ld a Q T L C h r. 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